ArticleComputational and structural biotechnology journal2021
Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR).
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.Chemical reviews · 2025Review
- Artificial intelligence in the management of metabolic disorders: a comprehensive review.Journal of endocrinological investigation · 2025Review
- The Experimentalist's Guide to Machine Learning for Small Molecule Design.ACS applied bio materials · 2024Review
- Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose-Tissue-Derived Human Mesenchymal Stem Cells EmployingMetabolites · 2023Article
- NMR Metabolomics Methods for Investigating Disease.Analytical chemistry · 2023Review
- Review
- Applications of machine learning in metabolomics: Disease modeling and classification.Frontiers in genetics · 2022Review
- Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra.Computational and structural biotechnology journal · 2022Article
- NMR-based metabolomics: Where are we now and where are we going?Progress in nuclear magnetic resonance spectroscopyReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolomics is an expanding field of medical diagnostics since many diseases cause metabolic reprogramming alteration. Additionally, the metabolic point of view offers an insight into the molecular mechanisms of diseases. Due to the complexity of metabolic assignment dependent on the 1D NMR spectral analysis, 2D NMR techniques are preferred because of spectral resolution issues. Thus, in this work, we introduce an automated metabolite identification and assignment from
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Registered trials
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